radiomics and image segmentation of urinary stones by artificial intelligence

Conditions: Urinary Stone; Renal Colic; Obstruction Ureter; Urosepsis; Urolithiasis; Ureter Stone; Kidney Stone

Summary: kidney stone disease causes significant morbidity, and stones obstructing the ureter can have serious consequences. imaging diagnostics with computed tomography (ct) are crucial for diagnosis, treatment selection, and follow-up. segmentation of ct images can provide objective data on stone burden and signs of obstruction. artificial intelligence (ai) can automate such segmentation but can also be used for the diagnosis of stone disease and obstruction. in this project, the aim is to investigate if: manual segmentation of ct scans can provide more accurate information about kidney stone disease compared to conventional interpretation. ai segmentation yields valid results compared to manual segmentation. ai can detect ureteral stones and obstruction or predict spontaneous passage.

background: goals and objectives: the project aims to contribute to personalized and improved treatment and follow-up of patients with kidney stones using radiomics and the development of an artificial intelligence tool for ct examination assessment. the objectives are to assess: * whether manual segmentation of ct images of the urinary tract provides equivalent or more accurate information about kidney stone disease compared to conventional interpretation and reporting. * whether segmentation performed with ai yields valid results compared to manual segmentation. * whether ai can detect ureteral stones and obstruction and/or predict spontaneous passage of stones. method: cohort: patients are recruited to the study at oslo university hospital, radiology department, section aker, which performs approximately 1350 ct examinations for urinary tract stones in approximately 1000 patients each year. approximately 500 patients with a new episode or newly occurring colic pain and clinical suspicion of kidney stones are expected to be included. clinical data (where available): * baseline ct: date and image data * initial treatment (conservative, urs, pcn, eswl) decision after baseline ct * follow-up ct: date and image data * time to spontaneous stone passage (negative control ct) or completed surgical intervention (urs) * any other surgical/invasive procedure * stone chemical analysis * clinical biochemistry: creatinine/egfr, crp, leukocytes (at baseline and follow-ups). image data: clinical radiology report: * stone: (largest calculus and any obstructing calculus): largest diameter in any plane, density (roi set by clinical judgment, largest possible roi - in the slice where the stone is largest), location (upper ureter: above crossing of vessels, lower ureter: below crossing of vessels, ostial: in bladder wall) * renal pelvis: largest diameter of calyx neck lower calyx, clinical assessment of dilation (not dilated/slight/moderate/severe). * segmentation: * stone: total segmented stone volume, largest diameter, and density of segmented stone. * collecting system: total segmented volume of the collecting system and renal pelvis.

Eligibility criteria:
inclusion criteria: * referral for ct due to episode of renal colic and clinical suspicion of urinary stone disease or * referral for ct due to new episode of pain in patient with known urinary stone disease * age ≥ 18 years exclusion criteria: * referral for control ct of asymptomatic patients with known urinary stone disease * referral for control ct after treatment * referral for control ct for spontaneous passage of stone. * lack of informed consent for any reason.

Keywords: urolithiasis computed tomography image segmentation ureteral obstruction artificial intelligence